Automated Supply Chain Risk Management with AI

Authors

  • Dr. François Garnier Professor of Geomatics Engineering, Université de Montréal, Canada Author

Keywords:

Automated Supply Chain, Risk Management, AI

Abstract

Supply chains are increasingly complex systems within a rapidly changing environment. As a result, organizations are vulnerable to a myriad of risks, from geopolitical and economic to social and environmental risks, and many others. Organizations today are, therefore, presented with a constantly expanding list of threats that pose a danger to the achievement of their strategic goals. The development and practice of risk management strategies are of paramount importance to any company that is susceptible to the negative impacts of a potential disruption. Disruptions are now more frequent and severe than in the past and are capable of creating long-term damage. Therefore, the potential benefits for a company due to an improved understanding of its supply chain risks lead to operational resilience.

Downloads

Download data is not yet available.

References

S. Kumari, “AI-Driven Cybersecurity in Agile Cloud Transformation: Leveraging Machine Learning to Automate Threat Detection, Vulnerability Management, and Incident Response”, J. of Art. Int. Research, vol. 2, no. 1, pp. 286–305, Apr. 2022

Tamanampudi, Venkata Mohit. "A Data-Driven Approach to Incident Management: Enhancing DevOps Operations with Machine Learning-Based Root Cause Analysis." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 419-466.

Machireddy, Jeshwanth Reddy. "Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI." Hong Kong Journal of AI and Medicine 2.1 (2022): 10-36.

Singh, Jaswinder. "Sensor-Based Personal Data Collection in the Digital Age: Exploring Privacy Implications, AI-Driven Analytics, and Security Challenges in IoT and Wearable Devices." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 785-809.

Tamanampudi, Venkata Mohit. "Natural Language Processing for Anomaly Detection in DevOps Logs: Enhancing System Reliability and Incident Response." African Journal of Artificial Intelligence and Sustainable Development 2.1 (2022): 97-142.

J. Singh, “How RAG Models are Revolutionizing Question-Answering Systems: Advancing Healthcare, Legal, and Customer Support Domains”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 850–866, Jul. 2019

Tamanampudi, Venkata Mohit. "AI and NLP in Serverless DevOps: Enhancing Scalability and Performance through Intelligent Automation and Real-Time Insights." Journal of AI-Assisted Scientific Discovery 3.1 (2023): 625-665.

Downloads

Published

02-12-2023

How to Cite

[1]
D. F. Garnier, “Automated Supply Chain Risk Management with AI”, Distrib Learn Broad Appl Sci Res, vol. 9, pp. 423–437, Dec. 2023, Accessed: Nov. 15, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/178

Similar Articles

51-60 of 123

You may also start an advanced similarity search for this article.